基于多组学数据识别癌症驱动通路的模型和算法

doi:10.11896/j.issn.1002-137X.2019.09.047

计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 310-314.doi: 10.11896/j.issn.1002-137X.2019.09.047

基于多组学数据识别癌症驱动通路的模型和算法

蔡齐荣¹, 吴璟莉^1,2

(广西师范大学计算机科学与信息工程学院广西桂林541004)¹;
(广西师范大学广西多源信息挖掘与安全重点实验室广西桂林541004)²

收稿日期:2018-07-20 出版日期:2019-09-15 发布日期:2019-09-02
通讯作者: 吴璟莉(1978-),女,博士,教授,硕士生导师,CCF会员,主要研究方向为生物信息学、算法设计与分析,E-mail:wjlhappy@mailbox.gxnu.edu.cn
作者简介:蔡齐荣(1993-),男,硕士生,主要研究方向为生物信息学、算法优化;
基金资助:
国家自然科学基金项目(61762015,61502111,61662007,61763003),广西自然科学基金项目(2015GXNSFAA139288,2016GXNSFAA380192),广西研究生教育创新计划项目(XYCSZ2018078),“八桂学者”工程专项,广西多源信息挖掘与安全重点实验室系统性研究基金项目(14-A-03-02,15-A-03-02),广西科技基地和人才专项(AD16380008)

Model and Algorithm for Identifying Driver Pathways in Cancer by Integrating Multi-omics Data

CAI Qi-rong¹, WU Jing-li^1,2

(College of Computer Science and Information Technology,Guangxi Normal University,Guilin,Guangxi 541004,China)¹;
(Guangxi Key Lab of Multi-source Information Mining & Security,Guangxi Normal University,Guilin,Guangxi 541004,China)²

Received:2018-07-20 Online:2019-09-15 Published:2019-09-02

摘要/Abstract

摘要： 通过整合体细胞突变、拷贝数变异和基因表达等3种组学数据,提出识别癌症驱动通路的改进最大权重子矩阵模型。该模型用通路中基因平均权重调控覆盖度和互斥度,对权重大的基因集覆盖度进行加强,同时放松其高互斥度约束。引入基于贪心算法的重组算子,提出求解该模型的单亲遗传算法PGA-MWS。采用胶质母细胞瘤和卵巢癌数据集对算法PGA-MWS和GA进行实验对比分析。实验结果显示,较GA方法,基于改进模型的PGA-MWS算法能识别出覆盖度高但互斥度不太高的基因集,且其识别的基因集中,许多均参与已知信号通路,并被证实与癌细胞密切相关,同时还能识别几种潜在的候选驱动通路,因此PGA-MWS方法可作为检测癌症驱动通路的一种有效补充。

关键词: 癌症, 多组学数据, 模型, 驱动通路, 算法

Abstract: This paper proposed improved maximum weight submatrix problem model for identifying driver pathways in cancer by integrating somatic mutations,copy number variations,and gene expressions.The model tries to adjust cove-rage and mutual exclusion with the average weight of genes in a pathway,enhances the coverage of the gene set with large weight and relaxes its mutual exclusion constraint.By introducing a greedy based recombination operator,a parthenogenetic algorithm PGA-MWS was presented to solve the model.Experimental comparisons between PGA-MWS and GA were performed on glioblastoma and ovarian cancer datasets.Experimental results show that,compared with GA algorithm,PGA-MWS algorithm based on the improved model can identify gene sets with high coverage and less mutual exclusion.Many of the identified gene sets are involved in known signaling pathways,and have been confirmed to be closely related to cancer cells.Simultaneously,several potential drive pathways can also be discovered.Therefore,the proposed approach may become a useful complementary one for identifying driver pathways.

Key words: Algorithm, Cancer, Driver pathway, Model, Multi-omics data

中图分类号:

TP301

蔡齐荣, 吴璟莉. 基于多组学数据识别癌症驱动通路的模型和算法[J]. 计算机科学, 2019, 46(9): 310-314. https://doi.org/10.11896/j.issn.1002-137X.2019.09.047

CAI Qi-rong, WU Jing-li. Model and Algorithm for Identifying Driver Pathways in Cancer by Integrating Multi-omics Data[J]. Computer Science, 2019, 46(9): 310-314. https://doi.org/10.11896/j.issn.1002-137X.2019.09.047

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基于多组学数据识别癌症驱动通路的模型和算法

Model and Algorithm for Identifying Driver Pathways in Cancer by Integrating Multi-omics Data

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